Third iteration of the movie mashup project. Now searching for specific words in a movie — also based on the subtitle.
I am not counting the words in Processing, so I copied and pasted the subtitles from “The Wolf of Wall Street” into Textalyser. The first words by far in the ranking are “you” and “what” — not very meaningful, though.

So I used “fucking,” third one with 211 occurrences. Taking a look at the full list, “fuck” and “fucking” also appear a lot of times. Because they are sort of variations of the same thing, I searched for any time that any of them is said — 431 times total. That happens in 351 subtitles — sometimes more than once in a single line, then.

Another iteration of the movie mashup project. This one searches for repeated lines in a movie. Also based on the subtitles file.
“Groundhog Day” was an obvious choice because of its repeating plot. Interesting to notice that some of the repeated scenes were certainly shot at once, because Bill Murray’s hair looks exactly the same.

This is the first attempt towards my proposal for a generative movie mashup. The code is based on the same Processing sketch I used before to make a mashup book.
The subtitles’ time code (start and end) are used to play the movie jumping from one position to another.
The lines are sorted alphabetically and the video editing is automated based on that.

Idea
A book made out of characters’ lines from two different movies. Their plots should have similar elements. As a result, mixing the two could create a coherent story.

InspirationThis project is influenced by the idea that narratives are constructed by readers — spectators in a broader sense, not necessarily text readers. This principle has been one of the central concepts explored in the Narrative Strategies class (see final project proposal for this class here). It can be also be interpreted as an example of the psychological phenomena apophenia, the human tendency to see patterns and create connections between apparently random information.

Content
I chose the movies “Her” and “Weird Science” to make this experiment. Though very different in language and target audience, both can be summed up as having the same basic plot. Besides, I think that the contrast between an 80’s teenage movie from John Hughes and a contemporary one by Spike Jonze was interesting.
At last, this relates to the idea of archetypal stories, another concept from narrative studies.

Prototypes
I downloaded the subtitles for the movies from Open Subtitles. They come in srt format, which is basically a plain text file in which the subtitles are indexed like this:1
00:01:45,294 --> 00:01:47,400
"To my Chris ...
2
00:01:49,333 --> 00:01:52,078
I've been thinking how to tell
how much you mean to me.

So I parsed each srt line using Processing and mapped the subtitle’s time index to a position. This is the first prototype. You can download the pdf here.

The lines for each movie are displayed in different colors. Because I was mapping time to position, the subtitles overlap most of the time. Though visual interesting, the result was barely readable. So I made a new version sorting the subtitles based on their time position and then displaying then in sequence. Here is the full pdf.

The colors helped keep it visually appealing, but they worked against my intention of mashing up the content. So I used black for all text, instead:

FinalI generated a pdf document with the result, making a script to paginate the content. Then I finished the book imposition and added some pages in Indesign. The colophon page tells the story of the book’s process in exaggerated detail, as a commentary on the questions about copyright that the project raises:

At last, I printed and binded the book, trying to make it as a “real” book as possible.

The result is sometimes pure nonsense and sometimes coincidently coherent. There are a few passages in which the characters seem to be talking about the same subject.

The code for all iterations of the project is on Github. It can easily be changed to mix any other two movies.
The full book pdf can be downloaded here.

This project is the third in a sequence of experiments with generative narrative that I have been doing for my Major Studio class.

The first one is called Telephone.

It is an html page in which you search for a term. The server connects to the Google Images API, gives you back an image, automatically search for the title of that image, gives you back the new image, search for its title and so on.

It might sound complex, but it’s a very simple way of mimicking the miscommunication created by the children’s game telephone — using computers instead of people.

The results generate unexpected connections. Sometimes they are completely nonsense.

Sometimes they seem to make sense.

When I first presented it, I didn’t explain the technicalities involved. Then I asked people how comfortable they felt about not really understanding what was going on behind the scenes.

The response I got was that the connections were very clear, so no further explanations were needed.

That seemed to connect with these 2 principles that I was learning in the Narrative Strategies class. The second principle might be interpreted as a consequence of the first.

In a broader sense, the idea of a message being formed by the person who receives it can also relate to the psychological phenomena known as apophenia, the human tendency to see patterns. The principle has its variants, such as pareidolia, a type of apophenia associated with images or sounds. This is a very common phenomena in our lives. We experience it by seeing faces in geometric forms like the one above.

We also experience it when we see Jesus’ faces on toasts.

An interesting manifestation of apophenia in literature is the short story “The Library of Babel,” by Jorge Luiz Borges. In the story, people inhabit a library full of books made out of 25 characters sorted in every possible order. Most books are unreadable and completely nonsense. Even so, people believe that there might be books containing useful information and even predictions of the future, because all possible permutations are contained in the books.

These ideas led me to a second experiment called Her Weird Science.

It is a mashup of Her and Weird Science — a teenage movie from the 80’s directed by John Hughes.

I chose the two inspired by the ideas of archetypal stories, that is, basic plots that repeat over different narrative pieces. Both Her and Weird Science tell the story of men playing God, recreating human intelligence. The same plot is also found in Frankenstein, Pinocchio, and in more recent pieces of science fiction.

This is very explicit in one of the initial scenes from Weird Science, in which the characters are watching Frankenstein.

That explains the content. The narrative strategy, though, is based on letting users construct their own stories in their minds. Technically, I mashed up the subtitles from the 2 different movies and sorted them based on the time index of each subtitle.

Then I printed a book with the result.

There is no visual differentiation telling users about which sentence belong to which movie. Like in the Telephone project, sometimes the result is pure nonsense and sometimes it seem to construct a cohesive narrative.

There are fortunate cases in characters from both movies talk about the same subject.

At last, my project for Narrative Strategies is a sequence of these two previous experiments. It will once again be a mashup of 2 movies.

Instead of translating that to a book form, though, I’ll keep them as videos.

Besides having similar stories, I’ll try to find movies with the same character names. By doing so, I’ll try to use the character names on the the subtitles to jump from one scene to another, instead of having the time-based sorting as before. This is a way of reducing the randomness in the connections.

I intend to do that with code once more. That is not a narrative strategy, but it’s not an idiosyncratic choice either. As said in the beginning of this presentation, I’m interested in generative art. The purpose is to create an apparently cohesive narrative by playing with algorithms.